PHuSe Lab, Department of Computer Science, University of Milano Statale, Via Celoria 18, 20133 Milan, Italy.
Department of Psychology, University of Milano-Bicocca, Piazza dell'Ateneo Nuovo 1, 20126 Milan, Italy.
Sensors (Basel). 2023 Jan 22;23(3):1262. doi: 10.3390/s23031262.
A principled approach to the analysis of eye movements for behavioural biometrics is laid down. The approach grounds in foraging theory, which provides a sound basis to capture the uniqueness of individual eye movement behaviour. We propose a composite Ornstein-Uhlenbeck process for quantifying the exploration/exploitation signature characterising the foraging eye behaviour. The relevant parameters of the composite model, inferred from eye-tracking data via Bayesian analysis, are shown to yield a suitable feature set for biometric identification; the latter is eventually accomplished via a classical classification technique. A proof of concept of the method is provided by measuring its identification performance on a publicly available dataset. Data and code for reproducing the analyses are made available. Overall, we argue that the approach offers a fresh view on either the analyses of eye-tracking data and prospective applications in this field.
为行为生物识别分析奠定了一个有原则的方法。该方法基于觅食理论，为捕捉个体眼球运动行为的独特性提供了坚实的基础。我们提出了一个组合的 Ornstein-Uhlenbeck 过程，用于量化描述觅食眼球行为的探索/利用特征。通过贝叶斯分析从眼动追踪数据中推断出复合模型的相关参数，结果表明该参数集可作为生物识别的合适特征集；最终通过经典分类技术实现生物识别。通过在公开数据集上测量其识别性能，提供了该方法的概念验证。可提供用于重现分析的数据和代码。总的来说，我们认为该方法为眼动追踪数据分析和该领域的预期应用提供了新的视角。
